Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound

• URL: http://arxiv.org/abs/2007.15273v1
• Date: Thu, 30 Jul 2020 07:23:55 GMT
• Title: Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound
• Authors: Yuhao Huang, Xin Yang, Rui Li, Jikuan Qian, Xiaoqiong Huang, Wenlong Shi, Haoran Dou, Chaoyu Chen, Yuanji Zhang, Huanjia Luo, Alejandro Frangi, Yi Xiong, Dong Ni
• Abstract summary: 3D ultrasound (US) is widely used due to its rich diagnostic information, portability and low cost. Standard plane (SP) localization in US volume not only improves efficiency and reduces user-dependence, but also boosts 3D US interpretation. We propose a novel Multi-Agent Reinforcement Learning framework to localize multiple uterine SPs in 3D US simultaneously.
• Score: 59.97366727654676
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D ultrasound (US) is widely used due to its rich diagnostic information, portability and low cost. Automated standard plane (SP) localization in US volume not only improves efficiency and reduces user-dependence, but also boosts 3D US interpretation. In this study, we propose a novel Multi-Agent Reinforcement Learning (MARL) framework to localize multiple uterine SPs in 3D US simultaneously. Our contribution is two-fold. First, we equip the MARL with a one-shot neural architecture search (NAS) module to obtain the optimal agent for each plane. Specifically, Gradient-based search using Differentiable Architecture Sampler (GDAS) is employed to accelerate and stabilize the training process. Second, we propose a novel collaborative strategy to strengthen agents' communication. Our strategy uses recurrent neural network (RNN) to learn the spatial relationship among SPs effectively. Extensively validated on a large dataset, our approach achieves the accuracy of 7.05 degree/2.21mm, 8.62 degree/2.36mm and 5.93 degree/0.89mm for the mid-sagittal, transverse and coronal plane localization, respectively. The proposed MARL framework can significantly increase the plane localization accuracy and reduce the computational cost and model size.

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